An efficient energy consumption and user comfort maximization methodology based on learning to optimization and learning to control algorithms

Abstract

Intelligent optimized energy management and prediction model in electric vehicles received attraction of the researchers in the last couple of years. Several techniques and models have been proposed in the literature for optimized energy management and control, but the trade-off between occupant comfort index and the energy consumption is still a significant challenge to the research community. In this paper, we have proposed a model based on learning to optimization and learning to control for user comfort maximization and efficient energy consumption. The proposed model is comprised of three layers; prediction module, learning to optimization module and learning to control module. In the prediction module, we have used the Kalman filter for noise removal and prediction of environmental parameters. In learning to optimization module, the bat algorithm has been used for user comfort maximization and energy consumption minimization. Furthermore, we have used the learning module with optimization module in order to tune the user preferences parameters in the comfort index formula used in the bat optimization algorithm. Likewise, the learning module has been used with the conventional fuzzy logic controller in order to improve its performance. In the conventional fuzzy logic controller, the membership functions boundaries are usually determined through hit and trial method, and once the membership functions are determined, they remain fixed for the entire process. In the learning to control module, the membership functions tuning is carried out. The membership functions are continuously tuned to get effective results. Experimental results indicate that the proposed method performs better as compared to the conventional methods and achieves improved user comfort with reduced energy consumption.